Paper Info
Title
Large-Deformation Image Registration of CT-TEE for Surgical Navigation of Congenital Heart Disease.
Abstract
The surgical treatment of congenital heart disease requires navigational assistance with transesophageal echocardiography (TEE); however, TEE images are often difficult to interpret and provide very limited anatomical information. Registering preoperative CT images to intraoperative TEE images provides surgeons with richer and more useful anatomical information. Yet, CT and TEE images differ substantially in terms of scale and geometry. In the present research, we propose a novel method for the registration of CT and TEE images for navigation during surgical repair of large defects in patients with congenital heart disease. Valve data was used for the coarse registration to determine the basic location. This was followed by the use of an enhanced probability model map to overcome gray-level differences between the two imaging modalities. Finally, the rapid optimization of mutual information was achieved by migrating parameters. This method was tested on a dataset of 240 images from 12 infant, children (<= 3 years old), and adult patients with congenital heart disease. Compared to the "bronze standard" registration, the proposed method was more accurate with an average Dice coefficient of 0.91 and an average root mean square of target registration error of 1.2655 mm.
Year
DOI
Venue
2018
10.1155/2018/4687376
COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE
Field
DocType
Volume
Computer vision,Probability model,Imaging modalities,Sørensen–Dice coefficient,Computer science,Mutual information,Artificial intelligence,Image registration,Heart disease
Journal
2018
ISSN
Citations 
PageRank 
1748-670X
0
0.34
References 
Authors
11
6
Name
Order
Citations
PageRank
Shuiping Gou111722.90
Linlin Chen200.34
Yu Gu3287.96
Liyu Huang4126.12
Meiping Huang524.79
Jian Zhuang610415.09